Reconciliation, is the process of comparing or matching transactions between two sets of data reported from two systems to ensure both systems recorded the same transactions correctly. As matching can involve multiple fields of the transaction, the rule based matching cannot cover all possible matching patterns especially where large alpha numeric fields are involved. On an average the match rate for business rule based matching ranges from 85% to 90%. The discrepancies may be referred as exceptions, outstandings, differences and so on. For the discrepancies, the reconciliation system needs to support manual matching and reduce the number of exceptions. Genuine exceptions need to be investigated by the relevant business lines where the transactions originated,
These exceptions need to be handled manually by observing, analyzing and matching by searching through the complete exception-data. This manual matching process involves visually looking at unmatched transactions and identifying possible matches. The reconciliation process is applied for very large volumes of data sets such as bank transaction data and the like. Thus, for such huge data sets, equally high volume of exceptions are generated. Thus, manual matching is very time consuming and error prone task.
Attempts to assist manual managing of these exceptions and reduce the human effort involved will be appreciated. A reconciliation system providing a workspace and backend analysis to assist manual matching will be appreciated.